rolling_std(rets, 250, min_periods=20) NameError: name ’rets’ is not defined # cap at 3 * 1 year standard deviation In [209]: cap_level = 3 * np.sign(winz) * std_1year ------------------------------------ eb7c33338e> in () ----> 1 cap_level = 3 * np.sign(winz) * std_1year NameError: name ’winz’ is not defined In [210]: winz[np.abs(winz) > 3 * std_1year] = cap_level ---------------------------- in () ----> 1 winz[np.abs(winz) > 3 * std_1year] = cap_level NameError: name ’cap_level’ is not defined In [211]: winz_model = ols(y=winz[’AAPL’], x=winz.ix[:, [’GOOG’]]

rolling_std(rets, 250, min_periods=20) # cap at 3 * 1 year standard deviation In [209]: cap_level = 3 * np.sign(winz) * std_1year In [210]: winz[np.abs(winz) > 3 * std_1year] = cap_level In [211]: winz_model =

rolling_std(rets, 250, min_periods=20) # cap at 3 * 1 year standard deviation In [209]: cap_level = 3 * np.sign(winz) * std_1year In [210]: winz[np.abs(winz) > 3 * std_1year] = cap_level In [211]: winz_model =

projectid) Note: A default project id can be set using the command line: bq init. There is a hard cap on BigQuery result sets, at 128MB compressed. Also, the BigQuery SQL query language has some oddities fillna, FutureWarning added to fill • Renamed DataFrame.getXS to xs, FutureWarning added • Removed cap and floor functions from DataFrame, renamed to clip_upper and clip_lower for consistency with NumPy

projectid) Note: A default project id can be set using the command line: bq init. There is a hard cap on BigQuery result sets, at 128MB compressed. Also, the BigQuery SQL query language has some oddities fillna, FutureWarning added to fill • Renamed DataFrame.getXS to xs, FutureWarning added • Removed cap and floor functions from DataFrame, renamed to clip_upper and clip_lower for consistency with NumPy

fillna, FutureWarning added to fill • Renamed DataFrame.getXS to xs, FutureWarning added • Removed cap and floor functions from DataFrame, renamed to clip_upper and clip_lower for consistency with NumPy

style.Styler class pandas.formats.style.Styler(data, precision=None, table_styles=None, uuid=None, cap- tion=None, table_attributes=None) Helps style a DataFrame or Series according to the data with HTML fillna, FutureWarning added to fill • Renamed DataFrame.getXS to xs, FutureWarning added • Removed cap and floor functions from DataFrame, renamed to clip_upper and clip_lower for consistency with NumPy

style.Styler class pandas.formats.style.Styler(data, precision=None, table_styles=None, uuid=None, cap- tion=None, table_attributes=None) Helps style a DataFrame or Series according to the data with HTML fillna, FutureWarning added to fill • Renamed DataFrame.getXS to xs, FutureWarning added • Removed cap and floor functions from DataFrame, renamed to clip_upper and clip_lower for consistency with NumPy

optional] If string, the LaTeX table caption included as: \cap- tion{}. If tuple, i.e (“full caption”, “short caption”), the caption included as: \cap- tion[]{}. 3.1. Input/output

optional] If string, the LaTeX table caption included as: \cap- tion{}. If tuple, i.e (“full caption”, “short caption”), the caption included as: \cap- tion[]{}. 3.1. Input/output